Automatically and precisely generating highlight videos with artificial intelligence

ABSTRACT

Presented herein are systems, methods, and datasets for automatically and precisely generating highlight or summary videos of content. For example, in one or more embodiments, videos of sporting events may be digested or condensed into highlights, which will dramatically benefit sports media, broadcasters, video creators or commentators, or other short video creators, in terms of cost reduction, fast, and mass production, and saving tedious engineering hours. Embodiment of the framework may also be used or adapted for use to better promote sports teams, players, and/or games, and produce stories to glorify the spirit of sports or its players. While presented in the context of sports, it shall be noted that the methodologies may be used for videos comprising other content and events.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is related to and claims priority benefit under 35 USC § 119 to commonly-owned U.S. Pat. App. No. 63/124,832, filed on 13 Dec. 2020, entitled “AUTOMATICALLY AND PRECISELY GENERATING HIGHLIGHT VIDEOS WITH ARTIFICIAL INTELLIGENCE,” and listing Zhiyu Cheng, Le Kang, Xin Zhou, Hao Tian, and Xing Li as inventors, which patent document is incorporated by reference herein in its entirety and for all purposes.

BACKGROUND A. Technical Field

The present disclosure relates generally to systems and methods for computer learning that can provide improved computer performance, features, and uses. More particularly, the present disclosure relates to systems and methods for automatically generating a digest or highlights of content.

B. Background

With rapidly evolving Internet technologies and emerging tools, video content, such as sports-related or other event videos, generated online are increasing at an unprecedentedly rapid pace. Especially during the COVID-19 pandemic, the number of views of online videos surged as fans were not allowed to attend events at a venue, such as a stadium or arena. Creating highlight videos or other events-related videos often involves human efforts to manually edit the original untrimmed videos. For example, the most popular sports videos often comprise short clips of a few seconds, while for machines to understand the video and spot key events precisely is very challenging. Combined with the vast amount of original content that exists, it is very time-consuming and costly to digest the original content into appropriate highlight videos. Also, given limited time to view content, it is important for viewers to be able to have access to condensed content that appropriately captures the salient elements or events.

Accordingly, what is needed are systems and methods that can automatically and precisely generate digested or condensed video content, such as highlight videos.

BRIEF DESCRIPTION OF THE DRAWINGS

References will be made to embodiments of the disclosure, examples of which may be illustrated in the accompanying figures. These figures are intended to be illustrative, not limiting. Although the disclosure is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the disclosure to these particular embodiments. Items in the figures may not be to scale.

FIG. 1 depicts an overview of a highlight generation system, according to embodiments of the present disclosure.

FIG. 2 depicts an overview method for training a generation system, according to embodiments of the present disclosure.

FIG. 3 depicts a general overview of the dataset generation process, according to embodiments of the present disclosure.

FIG. 4 summarizes some of cloud-sourced text data of commentaries and labels, according to embodiments of the present disclosure.

FIG. 5 summarizes collected untrimmed game videos, according to embodiments of the present disclosure.

FIG. 6 depicts a user interface embodiment designed for a person to annotate an event time for a video, according to embodiments of the present disclosure.

FIG. 7 depicts a method for event time and video runtime correlating, according to embodiments of the present disclosure.

FIG. 8 visualizes an example of recognizing the timer digits in a game video, according to embodiments of the present disclosure.

FIG. 9 depicts a method for generating clips from an input video, according to embodiments of the present disclosure.

FIG. 10 depicts feature extraction, according to embodiments of the present disclosure.

FIG. 11 shows a pipeline to extract features, according to embodiments of the present disclosure.

FIG. 12 graphically depicts a neural network model that may be used to extract features, according to embodiments of the present disclosure.

FIG. 13 depicts feature extraction using a Slowfast neural network model, according to embodiments of the present disclosure.

FIG. 14 depicts a method for audio feature extraction and event of interest time prediction, according to embodiments of the present disclosure.

FIG. 15A shows an example of a raw audio wave, and FIG. 15B shows its corresponding mean-abs feature, according to embodiments of the present disclosure.

FIG. 16 depicts a method for predicting the time of an event of interest in a video, according to embodiments of the present disclosure.

FIG. 17 illustrates a pipeline for temporal localization, according to embodiments of the present disclosure.

FIG. 18 depicts a method for predicting likelihood of an event of interest in a video clip, according to embodiments of the present disclosure.

FIG. 19 illustrates a pipeline for action spotting prediction, according to embodiments of the present disclosure.

FIG. 20 depicts a method for predicting likelihood of an event of interest in a video clip, according to embodiments of the present disclosure.

FIG. 21 illustrates a pipeline for final time prediction using an ensemble neural network model, according to embodiments of the present disclosure.

FIG. 22 depicts goal spotting results compared to another method, according to embodiments of the present disclosure.

FIG. 23 shows goal spotting results for three (3) clips, according to embodiments of the present disclosure. Ensemble learning achieves the best results.

FIG. 24 depicts a simplified block diagram of a computing device/information handling system, according to embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following description, for purposes of explanation, specific details are set forth in order to provide an understanding of the disclosure. It will be apparent, however, to one skilled in the art that the disclosure can be practiced without these details. Furthermore, one skilled in the art will recognize that embodiments of the present disclosure, described below, may be implemented in a variety of ways, such as a process, an apparatus, a system, a device, or a method on a tangible computer-readable medium.

Components, or modules, shown in diagrams are illustrative of exemplary embodiments of the disclosure and are meant to avoid obscuring the disclosure. It shall also be understood that throughout this discussion that components may be described as separate functional units, which may comprise sub-units, but those skilled in the art will recognize that various components, or portions thereof, may be divided into separate components or may be integrated together, including, for example, being in a single system or component. It should be noted that functions or operations discussed herein may be implemented as components. Components may be implemented in software, hardware, or a combination thereof.

Furthermore, connections between components or systems within the figures are not intended to be limited to direct connections. Rather, data between these components may be modified, re-formatted, or otherwise changed by intermediary components. Also, additional or fewer connections may be used. It shall also be noted that the terms “coupled,” “connected,” “communicatively coupled,” “interfacing,” “interface,” or any of their derivatives shall be understood to include direct connections, indirect connections through one or more intermediary devices, and wireless connections. It shall also be noted that any communication, such as a signal, response, reply, acknowledgement, message, query, etc., may comprise one or more exchanges of information.

Reference in the specification to “one or more embodiments,” “preferred embodiment,” “an embodiment,” “embodiments,” or the like means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the disclosure and may be in more than one embodiment. Also, the appearances of the above-noted phrases in various places in the specification are not necessarily all referring to the same embodiment or embodiments.

The use of certain terms in various places in the specification is for illustration and should not be construed as limiting. A service, function, or resource is not limited to a single service, function, or resource; usage of these terms may refer to a grouping of related services, functions, or resources, which may be distributed or aggregated. The terms “include,” “including,” “comprise,” and “comprising” shall be understood to be open terms and any lists the follow are examples and not meant to be limited to the listed items. A “layer” may comprise one or more operations. The words “optimal,” “optimize,” “optimization,” and the like refer to an improvement of an outcome or a process and do not require that the specified outcome or process has achieved an “optimal” or peak state. The use of memory, database, information base, data store, tables, hardware, cache, and the like may be used herein to refer to system component or components into which information may be entered or otherwise recorded.

In one or more embodiments, a stop condition may include: (1) a set number of iterations have been performed; (2) an amount of processing time has been reached; (3) convergence (e.g., the difference between consecutive iterations is less than a first threshold value); (4) divergence (e.g., the performance deteriorates); and (5) an acceptable outcome has been reached.

One skilled in the art shall recognize that: (1) certain steps may optionally be performed; (2) steps may not be limited to the specific order set forth herein; (3) certain steps may be performed in different orders; and (4) certain steps may be done concurrently.

Any headings used herein are for organizational purposes only and shall not be used to limit the scope of the description or the claims. Each reference/document mentioned in this patent document is incorporated by reference herein in its entirety.

It shall be noted that any experiments and results provided herein are provided by way of illustration and were performed under specific conditions using a specific embodiment or embodiments; accordingly, neither these experiments nor their results shall be used to limit the scope of the disclosure of the current patent document.

It shall also be noted that although embodiments described herein may be within the context of sporting events, like soccer, aspects of the present disclosure are not so limited. Accordingly, the aspects of the present disclosure may be applied or adapted for use in other contexts.

A. General Introduction 1. General Overview

Presented herein are embodiments to automatically, massively, and precisely generate highlight videos. For sake of illustration, soccer games will be used. However, it shall be noted that embodiments herein may be used or adapted for use for other sports and to non-sports events, such as concerts, performances, speeches, presentations, news, shows, video games, games, sporting events, animations, social media posts, movies, etc. Each of these activities may be referred to as a happening or an event, and a highlight of a happening may be referred to as an event of interest, an occurrence, or a highlight.

Taking advantage of a large-scale multimodal dataset, state-of-the-art deep learning models were created and trained to detect an event or events in games, such as a goal—although over events of interest may also be used (e.g., penalty, injury, a fight, red card, corner kick, penalty kick, etc.). Also presented herein are embodiments of an ensemble learning module to boost the performance of event-of-interest spotting.

FIG. 1 depicts an overview of a highlight generation system, according to embodiments of the present disclosure. In one or more embodiments, large-scale cloud-sourced text data and untrimmed soccer game videos were collected and fed into a series of data processing tools to generate candidate long clips (e.g., 70 seconds—although other time lengths may be used) containing major game events of interest (e.g., a goal event). In one or more embodiments, a novel event-of-interest spotting pipeline precisely locates the moment of the event in the clip. Finally, embodiments can build one or more customized highlight videos/stories around detected highlights.

FIG. 2 depicts an overview method for training a generation system, according to embodiments of the present disclosure. To train a generation system, a large-scale multimodal dataset of event-related data must be obtained or generated (205) so that it can be used as training data. Because a video runtime may not correspond to the time in the event, in one or more embodiments, for each video of a set of training videos, time anchoring is performed (210) in order to correlate video runtime with event time. Metadata (e.g., commentaries and/or labels) and the correlated time obtained by time anchoring may then be used to identify (215) an approximate time of an event of interest to generate a clip from the video that includes the event of interest. By using clips rather than the entire video, the processing requirements are greatly reduced. For each clip, features are extracted (220). In one or more embodiments, a set of pre-trained models may be used to obtain the extracted features, which may be multimodal.

In one or more embodiments, for each clip, a final temporal value of the event of interest is obtained (225) using a neural network model. The neural network model may, in embodiments, be an ensemble module that receives features from the set of models and outputs the final temporal value. Given the predicted, final temporal value for each clip, the predicted, final temporal value is compared (230) with its corresponding ground-truth value to obtain a loss value; and the loss values may be used to update (235) the model.

Once trained, the generation system may be output and used to generate highlight video(s) given an input event video.

2. Related Work

In recent years, artificial intelligence has been applied to analyze video contents and generate videos. In sports analytics, many computer vision technologies are developed to understand sports broadcasts. Specifically in soccer, researchers propose algorithms to identify key game events and player actions, analyze pass feasibility using player's body orientation, incorporate both audio and video streams to detect events, recognize group activities on the field using broadcast stream and trajectory data, aggregate deep frame features to spot major game events, and leverage the temporal context information around the actions to handle the intrinsic temporal patterns representing these actions.

Deep neural networks are trained with large-scale datasets for various video understanding tasks. Recent challenges include finding temporal boundaries of activities or localizing the events in temporal domain. In soccer video understanding, some have defined the goal event as the moment that the ball crosses the goal line.

In one or more embodiments, this definition of a goal is adopted and state-of-the-art deep learning models and methods are leveraged, as well as audio stream processing techniques, plus an ensemble learning module is employed in embodiments to spot precisely the event in soccer video clips.

3. Some Contributions of Embodiments

In this patent document, embodiments of an automatic highlight generation system that can precisely identify an event occurrence in videos are presented. In one or more embodiments, the system may be used to generate highlight videos massively without conventional human editing efforts. Some of the contributions provided by one or more embodiments include, but are not limited to, the following:

-   -   A large-scale multimodal soccer dataset, which includes         cloud-sourced text data, high-definition videos, was created.         And, in one or more embodiments, various data processing         mechanisms were applied to parse, clean, and annotate the         collected data.     -   Multimodal data from multiple sources was aligned, and candidate         long video clips were generated by cutting the raw videos into         70-second clips using parsed labels from cloud-sourced         commentary data.     -   Embodiments of an event spotting pipeline are presented herein.         Embodiments extract advanced feature representations from         multiple perspectives, and temporal localization methods are         applied to aid in spotting the event in the clips. Additionally,         embodiments were further designed with an ensemble learning         module to boost the performance of the event spotting. It shall         be noted that while the happening may be a soccer game and the         event-of-interest may be a goal, embodiments may be used for or         adapted for other happenings and other events-of-interest.     -   The experimental results show that the tested embodiments         achieve close to 1 accuracy (0.984) with tolerance of 5 seconds         in spotting the goal event in clips, which outperforms existing         work and establishes the new state-of-the-art. This result helps         to catch the exact goal moment and generate highlight videos         precisely.

4. Patent Document Layout

This patent document is organized as follows: Section B introduces the dataset that was created and how the data was collected and annotated. Embodiments of the methodology for building a highlight generation system embodiment and how to precisely spot the goal event in soccer video clips with the proposed methodologies are presented in Section C. Experimental results are summarized and discussed in Section D. It shall be reiterated that the use of a soccer game as the overall content and the goal as the event within that content is provided by way of illustration only, and one skilled in the art shall recognize that aspects herein may be applied to other content domains, including outside of the gaming domain, and to other events.

B. Data Processing Embodiments

To train and develop system embodiments, a large-scale multimodal dataset was created. FIG. 3 depicts a general overview of the dataset generation process, according to embodiments of the present disclosure. In one or more embodiments, one or more commentaries and/or labels associated with videos of the event are collected (305). For example, soccer game commentaries and labels (e.g., corner kick, goal, block shot, header, etc.) (see, e.g., labels and commentaries 105 in FIG. 1 ) from websites or other sources may be crawled to obtain data. Also, videos associated with the metadata (i.e., commentaries and/or labels) are also collected (305). For embodiments herein, high-definition (HD) untrimmed soccer game videos from various sources were collected. Amazon Mechanical Turk (AMT) was used to annotate (315) the game start time in untrimmed raw videos. In one or more embodiments, the metadata (e.g., commentary and/or label information) may be used (320) to help identify an approximate time of an event of interesting to generating a clip from the video that includes the event of interest (e.g., a clip of a goal). Finally, Amazon Mechanical Turk (AMT) was used to identify a precise time of the event of interest (e.g., a goal) in the processed video clips. The annotated goal time may be used as the ground truth during the training of embodiments of the goal spotting models.

1. Data Collection Embodiments

In one or more embodiments, sports websites were crawled for more than 1,000,000 commentaries and labels, which cover more than 10,000 soccer games from various leagues, dated from 2015 to 2020 seasons. FIG. 4 summarizes some of cloud-sourced text data of commentaries and labels, according to embodiments of the present disclosure.

The commentaries and labels provide a large amount of information for each game. For example, they include game date, team names, leagues, game events time (in minute), event labels such as goal, shot, corner, substitution, foul, etc., and associated player names. These commentaries and labels from cloud-sourced data may be translated into or may be considered as rich metadata for raw video processing embodiments, as well as highlight video generation embodiments.

Also collected were more than 2600 high-definition (720P or above) untrimmed soccer game videos from various online sources. The games come from various leagues, dated from 2014 to 2020. FIG. 5 summarizes collected untrimmed game videos, according to embodiments of the present disclosure.

2. Data Annotation Embodiments

In one or more embodiments, the untrimmed raw videos were first sent to the Amazon Mechanical Turk (AMT) workers to annotate the game start time (defined as the moment when the referee whistles to start the game), then the cloud-sourced game commentaries and labels were parsed to get the goal time in minutes for each game. By combining the goal minute labels and the game start times in the videos, candidate 70-second clips containing goal events were generated. Next, in one or more embodiments, these candidate clips were sent to the AMT for annotating the goal time in second. FIG. 6 depicts a user interface embodiment designed for AMT for goal time annotation, according to embodiments of the present disclosure.

For goal time annotation on AMT, each HIT (Human Intelligence Task, a single worker assignment) contained one (1) candidate clip. Each HIT was assigned to five (5) AMT workers, and the median timestamp value was collected as the ground truth label.

C. Methodology Embodiments

In this section, the details of embodiments of each of the highlighted generation system's five modules are presented. By way of brief overview, the first module embodiments in section C.1 are the game time anchoring embodiments, which examine the temporal integrity of the video and map any time in the game to time in the video.

The second module embodiments in section C.2 are the coarse interval extraction embodiments. This module is a major difference compared to commonly studied event spotting pipelines. In embodiments of this module, intervals of 70 seconds (although other size intervals may be used) are extracted, where a specific event is located by utilizing textual metadata. There are at least three reasons this approach is preferred compared to common end-to-end visual event spotting pipelines. First, clips extracted with metadata contain more context information and can be useful across different dimensions. With the metadata, the clips may be used as a temporal cut (such as game highlight videos) or may be used with other clips for the same team or player to generate team, player, and/or season highlight videos. The second reason is robustness, which comes from low event ambiguity of textual data. And third, by analyzing shorter clips for the event of interest rather than the entire video, many resources (processing, processing time, memory, energy consumption, etc.) are preserved.

Embodiments of the third module in system embodiments are the multimodal features extraction. Video features are extracted from multiple perspectives.

Embodiments of the fourth module are the precise temporal localization. Extensive studies of the technicalities of how embodiments of features extraction and temporal localization were designed and implemented are provided in sections C.3 and C.4, respectively.

Finally, embodiments of an ensemble learning module are described in section C.5.

1. Game Time Anchoring Embodiments

It was found that the event clocks in the event videos were sometimes irregular. The primary reason appeared to be that at least some of the event video files that were collected from the Internet contained damaged timestamps or frames. It was observed that in the video collections, about 10% of the video files contained temporal damages that temporally shift part of the video—sometimes by over 10 seconds. Some severe damages that were observed included over 100 seconds of missing frames. Besides errors in the video files, some unexpectedly rare events may have happened during happening/event, and the event clocks have to stop for a few minutes before they resume. Be it video content damage or game interruption, temporal irregularities may be viewed as temporal jumps, either forward or backward. To locate the clip of any event specified by the metadata precisely, in one or more embodiments, temporal jumps were detected, and calibrations were made accordingly. Therefore, in one or more embodiments, an anchoring mechanism was designed and used.

FIG. 7 depicts a method for event time and video runtime correlating, according to embodiments of the present disclosure. In one or more embodiments, OCR (optical character recognition) was performed (705) on the video frames at the interval of 5 seconds (but other intervals may be used) to read the game clock displayed in the video. The game start time in the video may be deduced (710) from the recognized game clock. Whenever a temporal jump occurred, in one or more embodiments, a record of the game time after the temporal jump was kept (710), and it was called or referred to as a time anchor. With time anchors, in one or more embodiments, any time in the game may be mapped (715) to time in the video (i.e., video runtime), and any clips specified by metadata may be precisely extracted. FIG. 8 visualizes an example of recognizing the timer digits in a game video, according to embodiments of the present disclosure.

As illustrated in FIG. 8 , the timer digits 805-820 may be recognized and correlated to the video runtime. Embodiments may collect multiple recognition results over time and may self-correct based on spatial stationarity and temporal continuity.

2. Coarse Interval Extraction Embodiments

FIG. 9 depicts a method for generating clips from an input video, according to embodiments of the present disclosure. In one or more embodiments, metadata from the cloud-sourced game commentaries and labels, which include the timestamps in minutes for the goal events, is parsed (905). Combined with the game start times detected by an embodiment of the OCR tool (discussed above), the raw videos may be edited to generate a x-second (e.g., 70 seconds) candidate clips containing an event of interest. In one or more embodiments, the extracting rule may be described by the following equations: t _({clipStart}) =t _({gameStart})+60*t _({goalMinute})−tolerance  (1) t _({clipEnd}) =t _({clipStart})+(base clip length+2*tolerance)  (1)

In one or more embodiments, given the goal minute t_({goalMinute}) and the game start time t_({gameStart}), clips from the t_({clipStart}) second in the video are extracted. In one or more embodiments, the duration of the candidate clips may be set to 70 seconds (in which the base clip length is 60 seconds and the tolerance is 5 seconds—although it should be noted that different values and different formulations may be used) because this covers the corner cases when the event of interest occurred very close to the goal minute, and it also tolerates small deviations of the OCR detected game start time. In the next section, method embodiments for spotting the goal second (the moment the ball crosses the goal line) in the candidate clips are presented.

3. Multimodal Features Extraction Embodiments

In this section, three embodiments to obtain advanced feature representations from the candidate clips are disclosed.

a) Feature Extraction with Pre-Trained Models Embodiments

FIG. 10 depicts feature extraction, according to embodiments of the present disclosure. Given the video data, in one or more embodiments, the temporal frames are extracted (1005) and resized (1010) in spatial domain, if needed to match in the input size, to feed a deep neural network model to obtain the advanced feature representations. In one or more embodiments, a ResNet-152 model pretrained on an image dataset is used, but other networks may be used. In one or more embodiments, the temporal frames are extracted at the inherent frame per second (fps) of the original video and then down sampled at 2 fps—i.e., ResNet-152 feature representations for 2 frames per second in the raw video are obtained. ResNet is a very deep neural network, which outputs a feature representation of 2048 dimensions per frame at the fully-connected-1000 layer. In one or more embodiments, the output of the layer before the softmax layer may be used as the extracted advanced features. Note the ResNet-152 may be used to extract advanced features from single images; it does not intrinsically embed temporal context information. FIG. 11 shows a pipeline 1100 to extract advanced features, according to embodiments of the present disclosure.

b) Slowfast Feature Extractor Embodiments

As part of the video features extractor, in one or more embodiments, a Slowfast network architecture—such as like that proposed by Feichtenhofer et al. (Feichtenhofer, C., Fan, H., Malik, J., & He, K., Slowfast Networks for Video Recognition, In Proceedings Of The IEEE International Conference On Computer Vision (pp. 6202-6211) (2019), which is incorporated by reference herein in its entirety) or by Xiao et al. (Xiao et al., Audiovisual SlowFast Networks for Video Recognition, available at arxiv.org/abs/2001.08740v1 (2020), which is incorporated by reference herein in its entirety)—may be used; although it shall be noted that other network architectures may be used. FIG. 12 graphically depicts a neural network model that may be used to extract features, according to embodiments of the present disclosure.

FIG. 13 depicts feature extraction using a Slowfast neural network model, according to embodiments of the present disclosure. In one or more embodiments, a Slowfast network is initialized (1305) with pre-trained weights using a training dataset. The network may be finetuned (1310) as a classifier. The second column in TABLE 1, below, shows the events classification results using the baseline network with a test data set. In one or more embodiments, the feature extractors are used to classify 4-second clips into 4 categories: 1) far from the event of interest (e.g., a goal), 2) just before the event of interest, 3) the event of interest, and 4) just after the event of interest.

Several techniques may be implemented to find the best classifier, which is evaluated by the top 1 error percentage. First, a network like that constructed in FIG. 12 is applied, which adds audio as an extra pathway to the Slowfast network (AVSlowfast). The network's visual part may be initialized with the same weights. One can see that direct joint training of visual and audio features actually hurts the performance. This is discovered as a common issue when training the multimodal networks. In one or more embodiments, a technique of adding different loss functions for visual and audio modes respectively was applied, and the whole network was trained with a multi-task loss. In one or more embodiments, a linear combination of cross-entropy losses on the audio-visual result and each of the audio and visual branches may be used. The linear combination may be a weighted combination in which the weights may be learned or may be selected as hyperparameters. The best top 1 error result showed in the bottom row in Table 1 were obtained.

TABLE 1 Results on event classification. Algorithms Top 1 error % Slowfast 33.27 Audio only 60.01 AVSlowfast 40.84 AVSlowfast multi-task 31.82

In one or more embodiments of the goal spotting pipeline, one may take advantage of the feature extractor part of this network (AVSlowfast with multi-task loss). Therefore, an aim is to lower the top 1 error, which corresponds to stronger features.

c) Mean-abs Audio Feature Embodiments

By listening to the soundtrack of an event (e.g., a game play without live commentary), people can often decide when an event of interest has occurred simply according to the volume of the audience. Inspired by this observation, a simple approach to extract the key information about events of interest from the audios directly was developed.

FIG. 14 depicts a method for audio feature extraction and event of interest time prediction, according to embodiments of the present disclosure. In one or more embodiments, the absolute values of the audio waves are taken and down sample (1405) to 1 Hertz (Hz). This feature representation may be referred to as the mean-abs feature, as it represents the mean sound amplitude at each second. FIGS. 15A & 15B show an example of one clip's raw audio wave and its mean-abs feature, respectively, according to embodiments of the present disclosure.

For each clip, the maximum 1505 of this mean-absolute audio feature 1500B may be located (1410). By locating the maximum (e.g., maximum 1505) of this mean-abs audio feature and its corresponding time (e.g., time 1510) for clips in the test data set, a 79% accuracy (under 5 seconds tolerance) in event temporal localization was achieved.

In one or more embodiments, the mean-absolute audio feature (e.g., 1500B in FIG. 15B) may be treated as a likelihood prediction of the event of interest for the times in the clip. As will be discussed below, this mean-absolute audio feature may be a feature input into an ensemble model that predicts a final time within the clip of the occurrence of the event of interest.

4. Action Spotting Embodiments

To precisely spot the moment of a goal in soccer game videos, in one or more embodiments, temporal context information around the moment is incorporated to learn what happened in the videos. For example, before a goal event occurs, the goal player will shoot the ball (or a header) and the ball will move towards the goal gate. In some scenarios, the attacking and defensive players are gathered in the penalty area and are not far from the goal gate. After the goal event, usually the goal player will run to the sideline, hug with teammates, and there will also be celebration in the spectators and among the coaches. Intuitively, these patterns in the video can help the model to learn what happened and spot the moment of the goal event.

FIG. 16 depicts a method for predicting likelihood of an event of interest in a video clip, according to embodiments of the present disclosure. In one or more embodiments, to construct a temporal localization model, a temporal convolution neural network, which takes the extracted visual features as the input, is used (1605). In one or more embodiments, the input features may be the extracted features from one or more of the prior models discussed above. For each frame, it outputs a set of intermediate features, which mixes temporal information across the frames. Then, in one or more embodiments, the intermediate features are input (1610) into a segmentation module that generates segmentation scores, which are assessed by a segmentation loss function. A cross-entropy loss function may be used for a segmentation loss function:

$L = {- {\sum\limits_{i = 1}^{n}{t_{i}{\log\left( p_{i} \right)}}}}$

where t_(i) is the ground truth label, and p_(i) is the softmax probability for the ith class.

In one or more embodiments, the segmentation scores and the intermediate features are concatenated and fed (1615) into an action spotting module, which generates (1620) the spotting predictions (e.g., likelihood predictions across the span of the clip of the event of interest occurring at each time instant), which may be assessed through a YOLO-like action spotting loss function. An L2 loss function may be used for an action spotting loss function:

$L = {\sum\limits_{i = 1}^{n}\left( {y_{true} - y_{predicted}} \right)^{2}}$

FIG. 17 illustrates a pipeline for temporal localization, according to embodiments of the present disclosure. In one or more embodiments, the temporal CNN may comprise convolution layers, the segmentation module may comprise convolutional layers and batch normalization layers, and the action spotting model may comprise pooling layers and convolutional layers.

In one or more embodiments, the model embodiment is trained with the segmentation and action spotting loss function as described by Cioppa et al. (A., Deliège, A., Giancola, S., Ghanem, B., Droogenbroeck, M. V., Gade, R., & Moeslund, T. in “A Context-Aware Loss Function for Action Spotting in Soccer Videos,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13123-13133, which is incorporated by reference herein in its entirety) considering the temporal context information. In one or more embodiments, segmentation loss is used to train the segmentation module, where each frame is associated with a score to represent how likely the frame belongs to an action class, while the action spotting loss is used to train the action spotting module, where a temporal location is predicted for an action class.

At least one of the major differences between embodiments herein and approach of Cioppa et al. is that embodiments herein deal with short clips, while Cioppa et al. take the entire game videos as the input and thus it requires much longer time to process the video and extract features when implemented in real time.

In one or more embodiments, the extracted features input may be the extracted features from the ResNet model discussed above or the AVSlowFast multi-task model discussed above. Alternatively, for the AVSlowFast multi-task model, the segmentation parts of the action spotting model may be removed. FIG. 18 depicts a method for predicting likelihood of an event of interest in a video clip, according to embodiments of the present disclosure. In one or more embodiments, a temporal convolution neural network receives (1805) as input the extracted features from the AVSlowfast multi-task model. For each frame, it outputs a set of intermediate features, which mixes temporal information across the frames. Then, in one or more embodiments, the intermediate features are input (1810) into an action spotting module, which generates (1815) the spotting predictions (e.g., likelihood predictions across the span of the clip of the event of interest occurring at each time instant), which may be assessed through an action spotting loss function. FIG. 19 illustrates a pipeline for action spotting prediction, according to embodiments of the present disclosure.

5. Ensemble Learning Embodiments

In one or more embodiments, a single predicted time for the event of interest in the clip may be obtained from each of the three models discussed above (e.g., picking a maximum value). One of the predictions may be used or the predictions may be combined (e.g., averaged). Alternatively, an ensemble model may be used to combine information from each of the models in order to obtain a final prediction of the event of interest in the clip.

FIG. 20 depicts a method for predicting likelihood of an event of interest in a video clip, according to embodiments of the present disclosure, and FIG. 21 illustrates a pipeline for final time prediction, according to embodiments of the present disclosure. In one or more embodiments, the final accuracy may be enhanced in an ensemble fashion, aggregating the output of the three models/features described in the above subsections. In one or more embodiments, all three previous models' outputs, together with a positional coding vector, may be combined as the input (2005) of the ensemble module. The combining may be done using concatenation, e.g., 4 d-dimension vectors become a 4×d matrix. For the ResNet and the AVSlowfast multi-task models, the input may be their likelihood prediction outputs from their action spotting models of Section 5, above. And, for the audio, the input may be the mean-absolute audio feature of the clip (e.g., FIG. 15B). In one or more embodiments, the positional coding vector is a 1-D vector representing the time length of the clip (i.e., an index).

In one or more embodiments, a core of the ensemble module is an 18-layer 1-D ResNet with a regression head. Essentially, the ensemble module learns a mapping from multidimensional input features comprising multiple modalities to the final temporal location of the event of interest in the clip. In one or more embodiments, the final time value prediction from the ensemble model is output (2010) and may be compared with the ground truth time to compute a loss. The losses of the various clips may be used to update the parameters of the ensemble model.

6. Inference Embodiments

Once trained, the overall highlight generating system, such as depicted in FIG. 1 , may be deployed. In one or more embodiments, the system may additionally include an input that allows a user to select one or more parameters for the generated clips. For example, the user may select a specific player, a span of games, one or more events of interest (e.g., goals and penalties), and the number of clips that make the highlight video (or a length of time for each clip and/or the overall highlight compilation video). The highlight generating system may then access videos and metadata and generate the highlight compilation video by concatenating the clips. For example, the user may want 10 seconds per event of interest clip. Thus, in one or more embodiments, the customized highlight video generation module may take the final predicted times for clips and select 8 seconds before the event of interest and 2 seconds after. Alternatively, as illustrated in FIG. 1 , key events of a player's career may be the events of interest and they may be automatically identified and compiled into a “story” of the player's career. Audio and other multimedia features may be added to the video by the customized highlight video generation module, which audio and features may be selected by the user. One skilled in the art shall recognize other applications of the highlight generation system.

D. Experimental Results

It shall be noted that these experiments and results are provided by way of illustration and were performed under specific conditions using a specific embodiment or embodiments; accordingly, neither these experiments nor their results shall be used to limit the scope of the disclosure of the current patent document.

1. Goal Spotting

For fair comparison with the existing works, the tested model embodiments were trained with candidate clips containing goals extracted from games in the train set of a dataset, and validate/test with candidate clips containing goals extracted from games in the valid/test set of the dataset.

FIG. 22 shows a main result: to spot the goals in 70 seconds clips, the tested embodiment 2205 significantly outperforms the current state-of-the-art method 2210 referred to as the Context-Aware approach in spotting goals in soccer.

The intermediate prediction results are also shown, which were obtained by using three different features described in Section C.3 or C.4 and the final results predicted by the ensemble learning module described in Section C.5. The goal spotting results for 3 clips were stacked in the FIG. 23 . As shown in FIG. 23 , the final prediction outputs by the ensemble learning module embodiment are the best in terms of their closeness to the ground truth labels (illustrated with the dashed line ovals).

2. Some Discussion Notes

As shown in FIG. 22 , embodiments can achieve close to 1 accuracy (0.984) with tolerance of 5 seconds. This result is phenomenal since it can be used to correct mislabeling from text and sync with customized audio commentaries. It also helps to generate highlights precisely, and thus gives users/editors options to customize their videos around the exact goal moment. The pipeline embodiments may be naturally extended to catch the moment of other events such as corners, free kicks, and penalties.

It is again reiterated that the use of a soccer game as the overall content and the goal as the event within that content is by way of illustration only, and one skilled in the art shall recognize that aspects herein may be applied to other content domains, including outside of the gaming domain, and to other events.

E. Computing System Embodiments

In one or more embodiments, aspects of the present patent document may be directed to, may include, or may be implemented on one or more information handling systems (or computing systems). An information handling system/computing system may include any instrumentality or aggregate of instrumentalities operable to compute, calculate, determine, classify, process, transmit, receive, retrieve, originate, route, switch, store, display, communicate, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data. For example, a computing system may be or may include a personal computer (e.g., laptop), tablet computer, mobile device (e.g., personal digital assistant (PDA), smart phone, phablet, tablet, etc.), smart watch, server (e.g., blade server or rack server), a network storage device, camera, or any other suitable device and may vary in size, shape, performance, functionality, and price. The computing system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, read only memory (ROM), and/or other types of memory. Additional components of the computing system may include one or more drives (e.g., hard disk drive, solid state drive, or both), one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, mouse, stylus, touchscreen and/or video display. The computing system may also include one or more buses operable to transmit communications between the various hardware components.

FIG. 24 depicts a simplified block diagram of an information handling system (or computing system), according to embodiments of the present disclosure. It will be understood that the functionalities shown for system 2400 may operate to support various embodiments of a computing system—although it shall be understood that a computing system may be differently configured and include different components, including having fewer or more components as depicted in FIG. 24 .

As illustrated in FIG. 24 , the computing system 2400 includes one or more central processing units (CPU) 2401 that provide computing resources and controls the computer. CPU 2401 may be implemented with a microprocessor or the like, and may also include one or more graphics processing units (GPU) 2402 and/or a floating-point coprocessor for mathematical computations. In one or more embodiments, one or more GPUs 2402 may be incorporated within the display controller 2409, such as part of a graphics card or cards. The system 2400 may also include a system memory 2419, which may comprise RAM, ROM, or both.

A number of controllers and peripheral devices may also be provided, as shown in FIG. 24 . An input controller 2403 represents an interface to various input device(s) 2404, such as a keyboard, mouse, touchscreen, and/or stylus. The computing system 2400 may also include a storage controller 2407 for interfacing with one or more storage devices 2408 each of which includes a storage medium such as magnetic tape or disk, or an optical medium that might be used to record programs of instructions for operating systems, utilities, and applications, which may include embodiments of programs that implement various aspects of the present disclosure. Storage device(s) 2408 may also be used to store processed data or data to be processed in accordance with the disclosure. The system 2400 may also include a display controller 2409 for providing an interface to a display device 2411, which may be a cathode ray tube (CRT) display, a thin film transistor (TFT) display, organic light-emitting diode, electroluminescent panel, plasma panel, or any other type of display. The computing system 2400 may also include one or more peripheral controllers or interfaces 2405 for one or more peripherals 2406. Examples of peripherals may include one or more printers, scanners, input devices, output devices, sensors, and the like. A communications controller 2414 may interface with one or more communication devices 2415, which enables the system 2400 to connect to remote devices through any of a variety of networks including the Internet, a cloud resource (e.g., an Ethernet cloud, a Fiber Channel over Ethernet (FCoE)/Data Center Bridging (DCB) cloud, etc.), a local area network (LAN), a wide area network (WAN), a storage area network (SAN) or through any suitable electromagnetic carrier signals including infrared signals. As shown in the depicted embodiment, the computing system 2400 comprises one or more fans or fan trays 2418 and a cooling subsystem controller or controllers 2417 that monitors thermal temperature(s) of the system 2400 (or components thereof) and operates the fans/fan trays 2418 to help regulate the temperature.

In the illustrated system, all major system components may connect to a bus 2416, which may represent more than one physical bus. However, various system components may or may not be in physical proximity to one another. For example, input data and/or output data may be remotely transmitted from one physical location to another. In addition, programs that implement various aspects of the disclosure may be accessed from a remote location (e.g., a server) over a network. Such data and/or programs may be conveyed through any of a variety of machine-readable medium including, for example: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as compact disc (CD) and holographic devices; magneto-optical media; and hardware devices that are specially configured to store or to store and execute program code, such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), flash memory devices, other non-volatile memory (NVM) devices (such as 3D XPoint-based devices), and ROM and RAM devices.

Aspects of the present disclosure may be encoded upon one or more non-transitory computer-readable media with instructions for one or more processors or processing units to cause steps to be performed. It shall be noted that the one or more non-transitory computer-readable media shall include volatile and/or non-volatile memory. It shall be noted that alternative implementations are possible, including a hardware implementation or a software/hardware implementation. Hardware-implemented functions may be realized using ASIC(s), programmable arrays, digital signal processing circuitry, or the like. Accordingly, the “means” terms in any claims are intended to cover both software and hardware implementations. Similarly, the term “computer-readable medium or media” as used herein includes software and/or hardware having a program of instructions embodied thereon, or a combination thereof. With these implementation alternatives in mind, it is to be understood that the figures and accompanying description provide the functional information one skilled in the art would require to write program code (i.e., software) and/or to fabricate circuits (i.e., hardware) to perform the processing required.

It shall be noted that embodiments of the present disclosure may further relate to computer products with a non-transitory, tangible computer-readable medium that have computer code thereon for performing various computer-implemented operations. The media and computer code may be those specially designed and constructed for the purposes of the present disclosure, or they may be of the kind known or available to those having skill in the relevant arts. Examples of tangible computer-readable media include, for example: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as a CD and holographic devices; magneto-optical media; and hardware devices that are specially configured to store or to store and execute program code, such as ASICs, programmable logic devices (PLDs), flash memory devices, other non-volatile memory (NVM) devices (such as 3D XPoint-based devices), and ROM and RAM devices. Examples of computer code include machine code, such as produced by a compiler, and files containing higher level code that are executed by a computer using an interpreter. Embodiments of the present disclosure may be implemented in whole or in part as machine-executable instructions that may be in program modules that are executed by a processing device. Examples of program modules include libraries, programs, routines, objects, components, and data structures. In distributed computing environments, program modules may be physically located in settings that are local, remote, or both.

One skilled in the art will recognize no computing system or programming language is critical to the practice of the present disclosure. One skilled in the art will also recognize that a number of the elements described above may be physically and/or functionally separated into modules and/or sub-modules or combined together.

It will be appreciated to those skilled in the art that the preceding examples and embodiments are exemplary and not limiting to the scope of the present disclosure. It is intended that all permutations, enhancements, equivalents, combinations, and improvements thereto that are apparent to those skilled in the art upon a reading of the specification and a study of the drawings are included within the true spirit and scope of the present disclosure. It shall also be noted that elements of any claims may be arranged differently including having multiple dependencies, configurations, and combinations. 

What is claimed is:
 1. A computer-implemented method comprising: for each video from a set of one or more videos: performing time anchoring to correlate video runtime with timing of a happening captured in the video; generating a clip from the video that includes an event of interest by using metadata related to the happening and correlated time obtained by time anchoring to identify an approximate time of the event of interest that occurred at the happening; performing feature extraction on the clip; and obtaining a final temporal value of the event of interest in the clip using the extracted features and neural network model; for each clip from a set of clips generated from the set of one or more videos, comparing the final temporal value with a corresponding ground-truth value to obtain a loss value; and using the loss values to update the neural network model.
 2. The computer-implemented method of claim 1 wherein the step of performing time anchoring to correlate video runtime with timing of a happening captured in the video further comprising: using optical character recognition on a set of video frames of the video to read time of a clock displayed in the video; given recognized time of the clock, generating a set of time anchors, which comprises a start time of the happening and any temporal shifts; and generating a time mapping that maps time of the clock with the video runtime using at least some of the time anchors in the set of time anchors.
 3. The computer-implemented method of claim 1 wherein the step of generating a clip from the video that includes the event of interest by using metadata related to the happening and the correlated time obtained by time anchoring to identify an approximate time of the event of interesting that occurred at the happening comprises: for a video, parsing data from metadata to obtain approximate timestamp for the event of interest; and using the approximate timestamp for the events of interest in the video and a time mapping to generate one or more candidate clips, in which a candidate clip includes an event of interest.
 4. The computer-implemented method of claim 1 wherein the steps of performing feature extraction on the clip and obtaining a final temporal value of the event of interest in the clip using the extracted features and neural network model comprise: performing feature extraction on the clip using a set of two or more models; and obtaining the final temporal value of the event of interest in the clip using an ensemble neural network model that receives input related to the features from the set of two or more models and outputs the final temporal value.
 5. The computer-implemented method of claim 4 wherein the set of two or more models comprises: a neural network model that extracts video features from the clip; a multimodal features neural network model that utilized video and audio information in the clip to generate multimodal-based features; and an audio feature extractor that generates a feature for the clip based upon audio levels in the clip.
 6. The computer-implemented method of claim 4 wherein the ensemble neural network model receives as input: a first likelihood prediction feature for the clip that is obtain as an output from an action spotting model system that received extracted features from a neural network model that extracts video features from the clip; a second likelihood prediction feature for the clip that is obtain as an output from an action spotting with segmentation model system that received extracted features from a multimodal features neural network model that utilized video and audio information in the clip to generate multimodal-based features; and a mean-absolute audio feature that is obtained from an audio feature extractor that generates the mean-absolute audio feature for the clip based upon audio levels in the clip.
 7. The computer-implemented method of claim 6 further comprising: combining a positional coding representation with the first likelihood prediction feature, the second likelihood prediction feature; and the mean-absolute audio feature as input to the ensemble neural network model.
 8. A system comprising: one or more processors; and a non-transitory computer-readable medium or media comprising one or more sets of instructions which, when executed by at least one of the one or more processors, causes steps to be performed comprising: for each video from a set of one or more videos: performing time anchoring to correlate video runtime with timing of a happening captured in the video; generating a clip from the video that includes an event of interest by using metadata related to the happening and correlate time obtained by time anchoring to identify an approximate time of the event of interest that occurred at the happening; performing feature extraction on the clip; and obtaining a final temporal value of the event of interest in the clip using the extracted features and a neural network model; and using the final temporal value of the event of interest to make shorter clip that includes the event of interest.
 9. The system of claim 8 wherein the step of performing time anchoring to correlate video runtime with timing of a happening captured in the video further comprising: using optical character recognition on a set of video frames of the video to read time of a clock displayed in the video; given recognized time of the clock, generating a set of time anchors, which comprises a start time of the happening and any temporal shifts; and generating a time mapping that maps time of the clock with the video runtime using at least some of the time anchors in the set of time anchors.
 10. The system of claim 8 wherein the step of generating a clip from the video that includes the event of interest by using metadata related to the happening and the correlated time obtained by time anchoring to identify an approximate time of the event of interesting that occurred at the happening comprises: for a video, parsing data from metadata to obtain approximate timestamp for the event of interest; and using the approximate timestamp for the events of interest in the video and a time mapping to generate one or more candidate clips, in which a candidate clip includes an event of interest.
 11. The system of claim 8 wherein the steps of performing feature extraction on the clip and obtaining a final temporal value of the event of interest in the clip using the extracted features and a neural network model comprise: performing feature extraction on the clip using a set of two or more models; and obtaining the final temporal value of the event of interest in the clip using an ensemble neural network model that receives input related to the features from the set of two or more models and outputs the final temporal value.
 12. The system of claim 11 wherein the set of two or more models comprises: a neural network model that extracts video features from the clip; a multimodal features neural network model that utilized video and audio information in the clip to generate multimodal-based features; and an audio feature extractor that generates a feature for the clip based upon audio levels in the clip.
 13. The system of claim 11 wherein the ensemble neural network model receives as input: a first likelihood prediction feature for the clip that is obtain as an output from an action spotting model system that received extracted features from a neural network model that extracts video features from the clip; a second likelihood prediction feature for the clip that is obtain as an output from an action spotting with segmentation model system that received extracted features from a multimodal features neural network model that utilized video and audio information in the clip to generate multimodal-based features; and a mean-absolute audio feature that is obtained from an audio feature extractor that generates the mean-absolute audio feature for the clip based upon audio levels in the clip.
 14. The system of claim 13 wherein the non-transitory computer-readable medium or media further comprises one or more sets of instructions which, when executed by at least one of the one or more processors, further causes steps to be performed comprising: combining a positional coding representation with the first likelihood prediction feature, the second likelihood prediction feature; and the mean-absolute audio feature as input to the ensemble neural network model.
 15. The system of claim 8 wherein the non-transitory computer-readable medium or media further comprises one or more sets of instructions which, when executed by at least one of the one or more processors, further causes steps to be performed comprising: combining a set of short clips together to make a compilation highlight video.
 16. A computer-implemented method comprising: for each video from a set of one or more videos: performing time anchoring to correlate video runtime with timing of a happening captured in the video; generating a clip from the video that includes an event of interest by using metadata related to the happening and correlate time obtained by time anchoring to identify an approximate time of the event of interest that occurred at the happening; performing feature extraction on the clip; and obtaining a final temporal value of the event of interest in the clip using the extracted features and neural network model; and using the final temporal value of the event of interest to make shorter clip that includes the event of interest.
 17. The computer-implemented method of claim 16 wherein the step of performing time anchoring to correlate video runtime with timing of a happening captured in the video further comprising: using optical character recognition on a set of video frames of the video to read time of a clock displayed in the video; given recognized time of the clock, generating a set of time anchors, which comprises a start time of the happening and any temporal shifts; and generating a time mapping that maps time of the clock with the video runtime using at least some of the time anchors in the set of time anchors.
 18. The computer-implemented method of claim 16 wherein the step of generating a clip from the video that includes the event of interest by using metadata related to the happening and the correlated time obtained by time anchoring to identify an approximate time of an event of interesting that occurred at the happening comprises: for a video, parsing data from metadata to obtain approximate timestamp for the event of interest; and using the approximate timestamp for the events of interest in the video and a time mapping to generate one or more candidate clips, in which a candidate clip includes an event of interest.
 19. The computer-implemented method of claim 16 wherein the steps of performing feature extraction on the clip and obtaining a final temporal value of the event of interest in the clip using the extracted features and neural network model comprise: performing feature extraction on the clip using a set of two or more models; and obtaining the final temporal value of the event of interest in the clip using an ensemble neural network model that receives input related to the features from the set of two or more models and outputs the final temporal value.
 20. The computer-implemented method of claim 19 wherein the ensemble neural network model receives as input: a first likelihood prediction feature for the clip that is obtain as an output from an action spotting model system that received extracted features from a neural network model that extracts video features from the clip; a second likelihood prediction feature for the clip that is obtain as an output from an action spotting with segmentation model system that received extracted features from a multimodal features neural network model that utilized video and audio information in the clip to generate multimodal-based features; a mean-absolute audio feature that is obtained from an audio feature extractor that generates the mean-absolute audio feature for the clip based upon audio levels in the clip; and a positional coding representation. 